Method of system for generating a cluster instruction set

ABSTRACT

A system for generating a cluster combination instruction set using machine learning, the system comprising a computing device configured to generate, as a function of a received cluster, a plurality of physical transfer paths from a distinct plurality of initiation points to a single locale, wherein the cluster comprises a cluster of a plurality of alimentary elements, determine, as a function of the plurality of physical transfer paths, a physical transfer pattern, generate an objective function of the plurality of physical transfer paths as a function of a plurality of constraints, select a physical transfer path that minimizes objective function, determine a cluster combination instruction set for the physical transfer pattern to the single destination, and generate a representation of the cluster combination instruction set via a graphical user interface to at least a physical transfer apparatus and the plurality of alimentary element originators.

FIELD OF THE INVENTION

The present invention generally relates to the field ofmachine-learning. In particular, the present invention is directed tomethods and systems for generating a cluster instruction set.

BACKGROUND

Efficient path selection using path guidance is an increasingly vitalprocess for provisioning of alimentary elements. However, existingmethods for path selection using path guidance suffer from inaccuracy inproviding a unifying system for a plurality of users at a single localeplacing orders from a plurality of alimentary element originators.

SUMMARY OF THE DISCLOSURE

In an aspect, A system for generating a cluster combination instructionset using machine learning, the system comprising a computing deviceconfigured to generate, as a function of a received cluster, a pluralityof physical transfer paths from a distinct plurality of initiationpoints to a single locale, wherein the cluster comprises a cluster of aplurality of alimentary elements, determine, as a function of theplurality of physical transfer paths, a physical transfer pattern,generate an objective function of the plurality of physical transferpaths as a function of a plurality of constraints, select a physicaltransfer path that minimizes objective function, determine a clustercombination instruction set for the physical transfer pattern to thesingle destination, and generate a representation of the clustercombination instruction set via a graphical user interface to at least aphysical transfer apparatus and the plurality of alimentary elementoriginators.

In another aspect, a method for generating a cluster combinationinstruction set using machine learning, the method comprising acomputing device configured for generating, as a function of a receivedcluster, a plurality of physical transfer paths from a distinctplurality of initiation points to a single locale, wherein the clustercomprises a cluster of a plurality of alimentary elements, determining,as a function of the plurality of physical transfer paths, a physicaltransfer pattern, generate an objective function of the plurality ofphysical transfer paths as a function of a plurality of constraints,selecting a physical transfer path that minimizes objective function,determine a cluster combination instruction set for the physicaltransfer pattern to the single destination, and generating arepresentation of the cluster combination instruction set via agraphical user interface to at least a physical transfer apparatus andthe plurality of alimentary element originators.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system ofgenerating a cluster instruction set;

FIGS. 2A-2B are diagrammatic representations of exemplary embodiments ofa user device for providing a cluster instruction set;

FIG. 3 is a block diagram of an exemplary embodiment of a clusterdatabase;

FIG. 4 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 5 is a diagrammatic representation of an exemplary embodiment of auser device providing an audiovisual notification;

FIG. 6 is a block diagram of an exemplary embodiment of a method forgenerating a cluster instruction set; and

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for generating a cluster instruction set. In anembodiment, a computing device is configured to receive a cluster of aplurality of alimentary elements from at least a user destined for asingle locale. Computing device may be configured to notify a pluralityof users at the single locale, via a graphical user interface, to submitadditional alimentary elements to the cluster according to each user'sunique alimentary element program. Computing device may utilize amachine-learning process and an objective function, to generate aplurality of physical transfer paths as a function of a plurality ofconstraints involved in the physical transfer process. Computing devicemay rank the plurality of physical transfer paths using a rankingmachine-learning process which using a ranking function for eachphysical transfer path as a function of the physical transfer resourcesand time required. Computing device may determine a physical transferpattern by selecting a physical transfer path as a function of theranking and generating a cluster instruction set. Cluster instructionset may contain alimentary element originator-specific and physicaltransfer apparatus-specific instructions. Computing device is configuredto generate a representation of the cluster instruction set via agraphical user interface to at least a physical transfer apparatus andthe plurality of alimentary element originators.

Referring now to FIG. 1, an exemplary embodiment of a system 100 forgenerating a cluster instruction set is illustrated. System includes acomputing device 104. Computing device 104 may include any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Computing device 104 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting computing device 104 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device 104 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device 104 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Computing device 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Computingdevice 104 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

Continuing in reference to FIG. 1, computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Continuing in reference to FIG. 1, computing device 104 is configured toreceive a cluster of a plurality of alimentary elements for physicaltransfer to a single locale. An “alimentary element,” as used in thisdisclosure, is a meal, grocery item, food element, nutrition supplement,edible arrangement, or the like, that may be generated by a restaurant,cafeteria, fast food chain, grocery store, food truck, farmer's market,proprietor, convenience store, deli, or any place that would have a needfor providing an alimentary item to a customer, client, patient, orindividual. A “cluster,” as used in this disclosure, is a plurality ofalimentary elements, associated with a plurality of orders, destined fora single locale at the same time. A cluster 108 may be a series ofdistinct alimentary elements for a plurality of users and/or a singleuser destined to a singular place. A cluster 108 may contain alimentaryelements that originate from a plurality of establishments, for instanceand without limitation, restaurants, grocery stores, food trucks, fastfood chains, convenience stores, and the like, including a plurality ofeach and/or any combination thereof. In non-limiting illustrativeexamples, a cluster 108 may be a burger from a fast food chain, a fruittray from a grocery store, candied yams from a gas station, an entréefrom a restaurant, and a protein drink from a health food store, whereinthe alimentary elements are destined to a single office building for aplurality of individuals. In further non-limiting illustrative examples,a cluster 108 may include an order of boneless chicken wings from arestaurant, a case of drinks from a grocery store, and a pizza from afast food chain ordered by a single user and destined for a singleresidence.

Continuing in reference to FIG. 1, a “physical transfer apparatus,” asused in this disclosure is any apparatus suitable for use as computingdevice 104, and that is associated with, incorporated in, and/oroperated by user, a vehicle, bike, drone, robot, autonomous vehicle,car, truck, etc. that is physically exchanging an alimentary elementfrom the originator to the user. A physical transfer apparatus 112 may avehicle operated by an individual to receive and physically transferalimentary elements. A physical transfer apparatus 112 may be anelectric-powered drone or robot that is equipped to pick up alimentaryelement packages for physical transfer.

Continuing in reference to FIG. 1, receiving a cluster 108 of aplurality of alimentary elements for physical transfer to a singlelocale may include generating an audiovisual notification in response toreceiving the cluster 108. An “audiovisual notification,” as used inthis disclosure is a graphical, textual, and/or sound-based notificationdisplayed to a user via a graphical user device, “smartphone”, heads-updisplay, laptop, tablet, internet-of-things (IOT) device, or the like.Audiovisual notification 116 may be displayed via a graphical userinterface, wherein the audiovisual notification 116 may includeinformation about submitting to the cluster 108 as a function of analimentary element program. An audiovisual notification 116 may includean alert that notifies a user that others at their locationcorresponding to building a cluster 108 of alimentary elements. A usermay receive an audiovisual notification 116 that notifies the user onthe status of at least an alimentary element that is part of a cluster108, wherein the status is information regarding the alimentary elementlocation, physical transfer apparatus, the status of the physicaltransfer, and the like.

Continuing in reference to FIG. 1, an “alimentary element program,” asused in this disclosure, is a plurality of alimentary elements that auser may be informed to select based on a user's biological extractiondata. An alimentary element program 120 may include, for instance andwithout limitation, an instruction set that a computing device 104 mayprovide to a user, via a graphical user interface, concerning alimentaryelements that may improve user's biological extraction parameters. Analimentary element program 120 may include alimentary elements a user isexpected to substitute to avoid ailments such as allergy, foodintolerances, inflammation, and the like. An alimentary element program120 may include alimentary elements a user is expected to include intheir diet to address nutrition deficiencies, symptoms, diseases, andthe like. In non-limiting illustrative examples, an alimentary elementprogram 120 may be associated with an audiovisual notification, whereinthe notification contains an alimentary element obtained from thealimentary element program 120. An alimentary element program 120 may bean alimentary element instruction set, as described above, and/or asdescribed in U.S. Nonprovisional application Ser. No. 16/375,303, filedon Apr. 4, 2019, and entitled “SYSTEM AND METHODS FOR GENERATINGALIMENTARY INSTRUCTION SETS BASED ON VIBRANT CONSTUTIONAL GUIDANCE,” theentirety of which is incorporated herein by reference.

Continuing in reference to FIG. 1, “biological extraction data,” as usedin this disclosure is any biological, chemical, physiological, etc. datathat is associated with and/or generated by the user. Biologicalextraction data may include medical histories, diseases, surgeries,injuries, symptoms, exercise frequency, sleep patterns, lifestylehabits, and the like, that may be used to inform a user's diet.Biological extraction data may include diet information such asnutrition deficiencies, food intolerances, allergies, and the like.Biological extraction data may alternatively or additionally include anydata used as a biological extraction as described in U.S. Nonprovisionalapplication Ser. No. 16/502,835, filed on Jul. 3, 2019, and entitled“METHODS AND SYSTEMS FOR ACHIEVING VIBRANT CONSTITUTION BASED ON USERINPUTS,” the entirety of which is incorporated herein by reference.

Continuing in reference to FIG. 1, the audiovisual notification 116 mayaddress a user to submit an alimentary element to the cluster 108,wherein the alimentary element corresponds to an alimentary elementprogram 120. Audiovisual notification 116 may be transmitted via agraphical user interface to each user of the plurality of users aboutsubmitting suitable alimentary elements to the cluster combination,wherein the alimentary elements correspond with their respectivealimentary element programs 120. A “suitable alimentary element,” asused in this disclosure, is an alimentary element that a user may selectas a function of an alimentary element program 120. A user may receivean audiovisual notification 116 concerning a cluster 108 at theirlocation, wherein the cluster 108 may be a queue that a plurality ofusers that have been notified to for submitting an alimentary element. Auser may receive an audiovisual notification 116, wherein thenotification directs the user to at least a suitable alimentary elementto add to the cluster 108. Computing device 104 may be configured tosend the notification and retrieve, for instance from a database, atleast a suitable alimentary element for a user.

Continuing in reference to FIG. 1, computing device 104 is configured todetermine, as a function of the plurality of physical transfer paths, aphysical transfer pattern. Cluster machine-learning process 124 may beany machine-learning algorithm performed by a machine-learning module,as described in further detail below. A “physical transfer pattern,” asused in this disclosure, is a determined physical transfer path for atleast a physical transfer apparatus to follow for obtaining allalimentary elements from all alimentary element originators for physicaltransfer to a single final location, including the number of physicaltransfer apparatuses, the order in which the alimentary elements are tobe obtained, and the transfer paths that all items are intended tofollow over a defined time, the location and time of any interchangenodes, and the times at which the alimentary element originators are togenerate each alimentary element. A “physical transfer path,” as used inthis disclosure, is a path that a physical transfer apparatus 112 mayfollow. A physical transfer path may be simply referred to, for thepurposes of this disclosure, as a “transfer path.” An “alimentaryelement originator,” as used in this disclosure, is an entity that mayprepare and/or generate an alimentary element for pickup by a physicaltransfer apparatus 112, as indicated here, such as a restaurant, fastfood chain, grocery store, food truck, and the like. An alimentaryelement originator may be simply referred to for the purposes of thisdisclosure as an “originator”. An alimentary element originator mayinclude a stationary originator and/or an originator with a specificlocation. An alimentary element originator may include an individualand/or a business.

Continuing in reference to FIG. 1, a physical transfer path may differin nature depending on the type of physical transfer apparatus 112indicated; for instance and without limitation a physical transfer pathfor a drone may include straight-line paths across a city, whereas atransfer path for a vehicle may abide by a city's street grid andtransportation infrastructure. A physical transfer pattern 128 mayinclude a single physical transfer path for a single physical transferapparatus and/or a plurality of physical transfer apparatuses 112. Aphysical transfer pattern 128 may include a plurality of physicaltransfer paths for a single physical transfer apparatus 112 and/or aplurality of physical transfer apparatuses 112. Cluster machine-learningprocess 124 may accept an input that is a cluster 108, wherein thecluster 108 contains a plurality of alimentary elements and a pluralityof alimentary element originators and a single final physical transferlocation, and generate an output which is a physical transfer pattern128. Cluster machine-learning process 124 may generate a plurality ofphysical transfer patterns 128, wherein the physical transfer patterns128 may differ in the number of resources used, for instance and withoutlimitation, the number of physical transfer apparatuses 112 used, thelengths of and/or number of the transfer paths, the order in whichalimentary elements are picked-up and/or generated, and the like.

Continuing in reference to FIG. 1, determining the physical transferpattern 128 includes generating a plurality of physical transfer pathsusing a cluster machine-learning process 124, wherein the clustermachine-learning process generates at least a physical transfer path foreach alimentary element of the cluster. Using the clustermachine-learning process 124 to generate a plurality of physicaltransfer paths to the single locale may include generating the physicaltransfer paths given a plurality of constraints. A “plurality ofconstraints,” as used in this disclosure, is a plurality of resourceconstraints, both physical and non-physical, that a machine-learningprocess uses to determine a physical transfer pattern 128 and tooptimize a physical transfer path. A plurality of constraints 132 mayinclude physical resource constraints such as the number of physicaltransfer apparatuses 112, the availability of physical transferapparatuses 112, geophysical data regarding each physical transferapparatus 112, the status of each alimentary element at each originator,etc. A plurality of constraints 132 may include non-physical resourcesconstraints such as times, for instance and without limitation, the timeelapsed since a cluster 108 is placed to a plurality of originators,expected times of arrival of physical transfer apparatuses 112, and/ortimes required for each physical transfer apparatus 112 to obtain eachalimentary element to its associated location. Physical transferpatterns 128 may differ in the navigation of the plurality ofconstraints, for instance and without limitation, making use of a singlephysical transfer apparatus 112 versus a plurality of physical transferapparatuses 112, wherein subsequent physical transfer patterns 128 willhave fewer available physical transfer apparatuses 112. In non-limitingillustrative examples, a plurality of physical transfer patterns 128 maydiffer in the amount of time required to complete physical transfer,wherein a first physical transfer pattern 128 may have an alimentaryelement order that minimizes standby time and a second physical transferpattern 128 minimizes driving distances. In further non-limitingillustrative examples, a plurality of physical transfer patterns 128 maydiffer in which physical transfer apparatuses 112 are selected andassigned to which transfer paths, for instance physical transferapparatuses 112 whose geophysical locations indicate closer startingproximity versus those which are more distant.

Continuing in reference to FIG. 1, the plurality of constraints 132 mayinclude geophysical data regarding each physical transfer apparatus.“Geophysical data,” as used in this disclosure is an address, longitudeand/or latitude position, global position system (GPS) coordinates, orthe like, that system 100 may use to identify the physical location of aphysical transfer apparatus, alimentary element, alimentary elementoriginator, user, and the like. Geophysical data regarding each physicaltransfer apparatus may include a “status” of each physical transferapparatus, wherein the status may include an indication that a physicaltransfer apparatus is nearby an alimentary element and/or suitable toretrieve alimentary element.

Continuing in reference to FIG. 1, using a cluster machine-learningprocess 124 to determine a physical transfer pattern 128 may includegenerating an identifier for each alimentary element of a cluster 108 ofa plurality of alimentary elements. An “identifier,” as used in thisdisclosure, is a qualitative and/or quantitative signifier for analimentary element that includes a package of data relevant to amachine-learning process for tracking the identity of the alimentaryelement and the associated data that places the alimentary elementwithin the context of all other alimentary elements of the clustercombination. Identifier 136 may include data regarding submissiontimestamp, user identity, and a designation of an alimentary elementoriginator. In non-limiting exemplary embodiments, an identifier 136 mayinclude the identity of the alimentary element; the identity of theassociated user; the identity and location of the associated alimentaryelement originator; the timestamp at which the order was submitted to acluster 108; the timestamp of when the alimentary element should beprepared and/or generated by the alimentary element originator; thetimestamp of when a physical transfer apparatus should pick-up analimentary element from the originator; the timestamp of when analimentary element is expected to arrive to a user; the position of analimentary element in a cluster 108, for instance alimentary element 1of 11 in the cluster 108; the position of an alimentary element in acluster 108 of a particular alimentary element originator, for instancealimentary element 1 of 3 from alimentary element originator #1. Innon-limiting illustrative examples, cluster machine-learning process 124may accept all the above pieces of data as inputs and generate anidentifier 136 which includes data attached to each alimentary elementin a physical transfer pattern 128. In further non-limiting illustrativeexamples, some data in an identifier 136, such as expected timestamps,may change between physical transfer patterns 128, depending on theplurality of constraints 132 and/or when a alimentary element isexpected to be prepared by an originator, received by a physicaltransfer apparatus 112, and the like.

Continuing in reference to FIG. 1, using a cluster machine-learningprocess 124 to determine a physical transfer pattern 128 may includedetermining, using the identifier 136 and the objective function, wheneach alimentary element originator should generate each alimentaryelement as a function of the plurality of constraints and the pluralityof physical transfer paths. In non-limiting exemplary embodiments,computing device 104 may compute a score associated with each physicaltransfer pattern 128 and select alimentary element order of pick-upand/or order of preparation, physical transfer apparatus 112, number ofphysical transfer apparatuses 112, and the like, to minimize and/ormaximize the score, depending on whether an optimal result isrepresented, respectively, by a minimal and/or maximal score; amathematical function, described herein as an “objective function,” maybe used by computing device 104 to score each possible pairing.Objective function may be based on one or more objectives, as describedbelow. Computing device 104 may pair a predicted transfer path 128, witha given physical transfer apparatus 112, that optimizes the objectivefunction. In various embodiments, a score of a particular physicaltransfer pattern 128 may be based on a combination of one or morefactors, including a plurality of constraints 132. Each factor may beassigned a score based on predetermined variables. In some embodiments,the assigned scores may be weighted or unweighted.

Continuing in reference to FIG. 1, computing device 104 configured todetermining the physical transfer pattern includes generating anobjective function of the plurality of physical transfer paths as afunction of a plurality of constraints, wherein minimizing the objectivefunction minimizes the plurality of physical transfer resources. As usedin this disclosure, “minimizing” signifies minimizing a difference froma goal representing a best solution, where the goal could be a maximaloutput, minimal output, or target number/set of numbers. Minimizingand/or optimizing an objective function may include minimizing thequantity of physical resources used, minimizing time for physicaltransfer, and the like. Alternatively or additionally, minimizingphysical resources used may refer to optimizing an objective function toachieve a maximal score, such as a maximal score in pairing aninterchange node time and location to a plurality of transfer paths.Minimizing physical resources may refer to optimizing an objectivefunction to achieve a specific range of values, or the like, wherein theoptimal solution is not a minimal value.

With continued reference to FIG. 1, minimization of objective functionmay include performing a greedy algorithm process. A “greedy algorithm”is defined as an algorithm that selects locally optimal choices, whichmay or may not generate a globally optimal solution. For instance,computing device 104 may select physical transfer apparatus so thatscores associated therewith are the best score for each alimentaryelement transfer path and/or for each physical transfer apparatus 112and/or plurality of physical transfer apparatuses 112. In such anexample, optimization of a greedy algorithm may determine thecombination of transfer paths such that each delivery of each pairingincludes the highest score possible but may not represent a globallyoptimal solution for the entire cluster combination.

Still referring to FIG. 1, objective function may be formulated as alinear objective function, which computing device 104 may solve using alinear program such as without limitation a mixed-integer program. A“linear program,” as used in this disclosure, is a program thatoptimizes a linear objective function, given at least a constraint. Forinstance, the availability of physical transfer apparatuses, thegeophysical location of physical transfer apparatuses, the order withwhich physical transfer apparatuses should pick-up alimentary elements,when an alimentary element originator should generate an alimentaryelement, among other constraints. In various embodiments, system 100 maydetermine a physical transfer pattern 128 that maximizes a total scoresubject to a constraint, as described above. A mathematical solver maybe implemented to solve for the set of physical transfer patterns 128that maximizes scores; mathematical solver may implement on computingdevice 104 and/or another device in system 100, and/or may beimplemented on third-party solver.

With continued reference to FIG. 1, optimizing objective function mayinclude minimizing a loss function, where a “loss function” is anexpression an output of which an optimization algorithm minimizes togenerate an optimal result. As a non-limiting example, computing device104 may assign variables relating to a set of parameters, which maycorrespond to score components as described above, calculate an outputof mathematical expression using the variables, and selects a physicaltransfer pattern 128 that produces an output having the lowest size,according to a given definition of “size,” of the set of outputsrepresenting each of plurality of candidate ingredient combinations;size may, for instance, included absolute value, numerical size, or thelike. Selection of different loss functions may result in identificationof different potential pairings as generating minimal outputs.

Continuing in reference to FIG. 1, objective function, as used in thisdisclosure, may refer to a program that optimizes a linear objectivefunction, given at least a constraint. For instance, and withoutlimitation, objective function may seek to maximize a total scoreΣ_(r∈R) Σ_(s∈S) c_(rs)x_(rs), where R is the set of all physicaltransfers patterns r, S is a set of all alimentary elements of a cluster108 s, c_(rs) is a score of a pairing of a given transfer path with agiven combination of alimentary elements, and x_(rs) is 1 if a route ris paired with physical transfer apparatus 112 s, and 0 otherwise.Continuing the example, constraints may specify that each alimentaryelement is assigned to only one physical transfer apparatus 112, andeach batch is assigned only one physical transfer apparatus 112.Physical transfer patterns 128 may be optimized for a maximum scorecombination of all generated combinations, with selection based on avalue indicating an optimized combination. In various embodiments,system 100 may determine combination of alimentary element transferpaths, originator times, and physical transfer apparatus 112 assignment,and the like, that maximizes a total score subject to a plurality ofconstraint that all deliveries are paired to exactly one physicaltransfer apparatus 112. Not all physical transfer apparatuses 112 mayreceive a physical transfer pattern 128 pairing since each delivery mayonly be delivered by one physical transfer apparatus 12; likewise, aphysical transfer pattern may receive a more optimal scoring byassigning more physical transfer apparatuses 112. A mathematical solvermay be implemented to solve for the set of feasible paths that maximizesthe sum of scores across all pairings; mathematical solver mayimplemented on computing device 104 and/or another device in system 100,and/or may be implemented on third-party solver.

Continuing in reference to FIG. 1, objective function may be implementedas described above, and/or as described in U.S. Nonprovisionalapplication Ser. No. 16/890,839, filed on Jun. 2, 2020, and entitled“METHODS AND SYSTEMS FOR PATH SELECTION USING VEHICLE ROUTE GUIDANCE,”the entirety of which is incorporated herein by reference. Amachine-learning process, such as a cluster machine-learning process 124may call such an algorithm and perform it for one or more steps ingenerating a physical transfer pattern 128 and/or optimizing thephysical transfer pattern 128, given a plurality of constraints 132.Persons skilled in the art, upon review of this disclosure in itsentirety, will be aware of the various methods and algorithms performedby a computing device to optimize a physical transfer patterns given aplurality of variables that represent constraints to the optimalsolution.

Continuing in reference to FIG. 1, computing device 104 minimizing theobjective function may include minimizing plurality of physical transferresources further comprises minimizing the number of physical transferapparatuses utilized while minimizing the amount of time to perform thephysical transfer pattern. Cluster machine-learning process 124 mayperform an objective function, as described above, to optimize aplurality of physical transfer patterns 128, wherein optimizing theplurality of physical transfer patterns 128 may include working towardan output that represents a single physical transfer pattern 128 of theplurality that minimizes the use of resources, including the number ofamount of physical transfer apparatuses 112 used and/or the amount oftime needed to complete each physical transfer pattern 128.

Continuing in reference to FIG. 1, using an objective function tominimize physical transfer resources may include optimizing a pluralityof variables involved in determining a physical transfer pattern 128.Optimizing may include, for instance, minimizing the number of physicaltransfer apparatuses used while minimizing the amount of time tocomplete a physical transfer pattern 128. Variables may include, thenumber of, availability of, and geophysical location of physicaltransfer apparatuses 112, the order of receiving alimentary elementsfrom originators, the time of originators generating each alimentaryelement, the transfer paths each physical transfer apparatus 112follows, and the like. Computing device 104 performing clustermachine-learning process 124 may use an objective function, as describedabove, wherein an optimal solution results in reducing the number ofphysical transfer apparatuses used to complete a physical transferpattern 128. Alternatively or additionally, cluster machine-learningprocess 124 may use an objective function to determine the values ofvariables associated with, for instance and without limitation, thetimes for preparing the alimentary elements, the order in which thealimentary elements are obtained, and the transfer paths taken, whereinthe objective function determines when alimentary elements are to begenerated in order to minimize the time required for a physical transferapparatus 112 to follow the transfer paths in obtaining them all.

In some instances, and still referring to FIG. 1, reducing the number ofphysical transfer apparatuses 112 may result in a concomitant increasein the amount of time required to complete the physical transfer pattern128. In such an instance, cluster machine-learning process 124 mayrecognize that such a tradeoff exists, wherein decreasing one parameter(number of physical transfer apparatuses 112) increases a secondparameter (time required for completing transfer paths). In such anexample, cluster machine-learning process 124 may determine from anidentifier a maximal threshold of time exists, wherein the maximalthreshold of time for a cluster 108 of alimentary elements must not beexceeded for physical transfer. For instance and without limitation,upon placing the cluster 108 of a plurality of alimentary elements, aplurality of users may receive an estimated timestamp describing when toexpect the alimentary elements. In such an example, this estimatedtimestamp may be included in the identifier associated with alimentaryelement, wherein the timestamp becomes a constraint that the clustermachine-learning process 124 ‘knows’ the optimized physical transferpattern 128 must not exceed. In such a non-limiting example, clustermachine-learning process 124 may then learn to minimize physicaltransfer apparatus resources, including vehicles, transfer paths,personnel, and the like, while keeping below a maximal time threshold inan identifier. If not possible with the current number of physicaltransfer apparatuses selected, cluster machine-learning process 124‘knows’ to increase by a discrete amount, such as by 1 physical transferapparatus, and re-calculate time requirements for completing physicaltransfer paths. Cluster machine-learning process 124 may iterativelyperform these optimization calculations, wherein the clustermachine-learning process 124 may ‘learn’ for each cluster 108 the numberof physical transfer apparatuses result in minimized time requirements,wherein the maximal time requirement may be used to minimize the numberof physical transfer apparatuses used.

Continuing in reference to FIG. 1, cluster machine-learning process 124may use an optimized physical transfer pattern 128, wherein the numberof physical transfer apparatuses 112 and time requirements areminimized, to inform when each alimentary element originator shouldprepare and/or generate each alimentary element. Clustermachine-learning process 124 may identify an optimal transfer path fromeach alimentary element to the final location for the optimized numberof physical transfer apparatuses 112, wherein the transfer path dictatesthe order in which each alimentary elements are picked-up, and thus whenthe alimentary element should be prepared and/or generated by theoriginator. Alternatively or additionally, cluster machine-learningprocess 128 may determine which a transfer path between each originatorand determine the order in which originators should prepare and/orgenerate each alimentary element for the physical transfer apparatus tofollow the optimal physical transfer pattern 128.

Continuing in reference to FIG. 1, determining a physical transferpattern 128 may include using a cluster machine-learning process 124 togenerate a plurality of candidate transfer paths 140, wherein eachcandidate physical transfer path 140 is a geophysical path that aphysical transfer apparatus 112 may follow to obtain an alimentaryelement and transfer to a single locale. A “geophysical path,” as usedin this disclosure, is a series of addresses, longitude and/or latitude,global position system (GPS) coordinates, or the like, which trace apath throughout a physical space such as a neighborhood, city, building,or the like, along which a physical transfer apparatus may follow.Cluster machine-learning process 124 may output, as a function ofoptimizing a physical transfer pattern 128, a plurality of candidatetransfer paths 140, wherein each output is a geophysical path that aphysical transfer apparatus 112, and/or a plurality of physical transferapparatuses may follow in concert to obtain all alimentary elements andtransfer to the single locale within a specific time frame.

Continuing in reference to FIG. 1, determining a physical transferpattern 128 may include using the cluster machine-learning process 124to identify at least an interchange node, wherein each interchange nodeof the at least an interchange node comprises a node location and a nodetime based on the physical locations of a plurality of physical transferapparatuses prior to physical transfer to a user location. An“interchange node,” as used in this disclosure, is a centralizedgeophysical location that can become a distribution center foralimentary elements to be loaded, swapped, or otherwise exchangedbetween physical transfer apparatuses, including a time associated withwhen physical transfer apparatuses are to arrive at the geophysicallocation. For instance and without limitation, interchange nodes 144 maybe parking lots, fuel stations, warehouses, storage centers, parks,convenience stores, or the like, that may accommodate physical transferapparatuses to take on alimentary elements for physical transfer. Aninterchange node 144 location and time for physical transfer to meet ata node location may be generated and/or selected by computing device 104based on candidate physical transfer path, physical transfer apparatusgeophysical location, transfer timestamps, and the like, as describedabove by cluster machine-learning process 128 and/or as described inU.S. Nonprovisional application Ser. No. 16/983,096, filed on Aug. 3,2020, and entitled “METHODS AND SYSTEMS FOR DETERMINING PHYSICALTRANSFER INTERCHANGE NODES,” the entirety of which is incorporatedherein by reference.

Continuing in reference to FIG. 1, determining a physical transferpattern 128 may include calculating a change in candidate physicaltransfer path 140 time and distance resulting from using the interchangenode 144. Computing device 104 may generate interchange node locations,as described above, wherein the cluster machine-learning process 124 mayaccept the interchange node as an input for outputting candidatephysical transfer paths 140. Cluster machine-learning process 124 mayinclude an interchange node 144 in candidate physical transfer paths 140and calculate, using any mathematical operation, such as subtraction, ifadding the interchange node 144 resulted in minimizing the number ofphysical transfer apparatuses 112 and/or minimizing the time requiredfor the physical transfer. Alternatively or additionally, computingdevice 104 may calculate difference between candidate physical transferpath 140 times and distances outputs, with and without an interchangenode 144 included, and iteratively determine if including theinterchange node minimizes physical resources. In the event that addingthe interchange node 144 results in a more optimal physical transferpattern 128, cluster machine-learning process 124 may generate morecandidate transfer paths 140 with additional interchange nodes 144. Theaddition of interchange nodes 144 may be done until the amount ofphysical resources are exceeded (not enough physical transferapparatuses available), and/or the time is no longer being minimizedwith adding interchange nodes 144.

Continuing in reference to FIG. 1, determining a physical transferpattern may include which physical transfer path and interchange node144 pairing of the plurality of candidate transfer paths 140 minimizesthe objective function. Computing device 104 may select the physicaltransfer path of the plurality of candidate physical transfer paths 140and interchange node 144 pairing that results in minimizing the physicalresources used, as described above. Computing device 104 may determine aphysical transfer pattern 128 which includes a plurality of candidatephysical transfer paths 140 and at least an interchange node 144,wherein pairing each physical transfer path and interchange node 144represents an optimal physical transfer.

Continuing in reference to FIG. 1, computing device 104 is configured toselect a physical transfer path that minimizes the objective function.In non-limiting illustrative examples, computing device 104 may use aranking machine-learning process 148, wherein a ranking machine-learningprocess 148 may be any machine-learning process and/or algorithm usedfor classification as described in further detail below, whereinclassification may be performed as a ranking of inputs to generateoutputs classified into a ranked list, provided a criterion for ranking.In non-limiting illustrative examples, the ranking may be a limitationlogistic regression and/or naive Bayes ranking algorithm, nearestneighbor algorithm such as k-nearest neighbors, support vector machines,least squares support vector machines, fisher's linear discriminant,quadratic classifiers, decision trees, boosted trees, random forestclassifiers, learning vector quantization, and/or neural network-basedalgorithms, as described herein. Ranking machine-learning process 148may accept an input that is a plurality of candidate transfer paths 140,a plurality of physical transfer patterns 128, and/or any otherdetermination of a system 100 as described herein, and generate anoutput that is a ranked list of outputs according to a ranking criteria.For instance and without limitation, a ranking criterion may be that thehighest ranking is reserved for the outcome that results in minimal timefor a physical transfer, or the least amount of physical transferapparatuses 112 required. A ranking process may include any of thefunctions described above, such as an objective function that may inputa plurality of objectives, such as a plurality of constraints, and rankthe objectives by a variety of factors, for instance without limitation,by number of physical transfer apparatuses used, time required forphysical transfer path, and the like, and output a first rank-orderedgoal set 124 ranked by that function. Computing device 104 may perform aranking algorithm using a machine-learning process, as described infurther detail below. Computing device 104 may perform a rankingfunction using any process, method, and/or algorithm as described hereinwherein ranking is based on physical transfer recourses and physicaltransfer time. In non-limiting illustrative examples, computing device104 may include interchange node 144 for a cluster machine-learningprocess 128 depending on if the ranking determines that adding aninterchange node 144 results in higher ranked candidate physicaltransfer paths 140 and/or higher ranked physical transfer patterns 128.

Continuing in reference to FIG. 1, determining which physical transferpath and/or interchange node 144 pairing represents an optimal physicaltransfer path may include selecting the optimal physical transfer pathwhich minimizes physical transfer resources and physical transfer timeas a function of the ranking. Computing device 104 may select thecandidate physical transfer path 140 and interchange node pairing thatresults in the optimal physical transfer path, wherein the optimalphysical transfer path minimizes physical transfer resources andphysical transfer time according to the ranking. In non-limitingillustrative examples, the candidate physical transfer path 140 andinterchange node pairing may include no interchange node 144. In furthernon-limiting illustrative examples, computing device 104 may select theoptimal candidate physical transfer path 140, wherein each candidatetransfer path 140 is for a single alimentary element and/or alimentaryelement originator. In such an instance, computing device 104 maygenerate the optimal physical transfer pattern 128 by selecting theoptimal candidate physical transfer path 140 for each individualalimentary element. Alternatively or additionally, computing device 104may generate the optimal physical transfer pattern 128 by selecting theoptimal candidate physical transfer path 140 for each clustercombination, wherein the candidate physical transfer path 140 is aphysical transfer path for all alimentary elements. Furthermore, withoutlimitation, the optimal candidate physical transfer path 140 may includeone or more interchange nodes 144 paired with the physical transferpath.

Continuing in reference to FIG. 1, computing device 104 is configured todetermine a cluster instruction set for the physical transfer pattern128 to the single destination. A “cluster instruction set,” as used inthis disclosure, is a series of steps and/or instructions associatedwith carrying out the physical transfer pattern 128, which includes thephysical transfer paths for at least a physical transfer apparatus 112to follow, the geolocation and times associated with any interchangenodes 144, the timestamps for alimentary element originators are togenerate alimentary elements, and the single destination for the cluster108. A cluster instruction set 152 may be generated for the optimalphysical transfer pattern, as selected by the computing device 104.

Continuing in reference to FIG. 1, determining a cluster instruction set152 for a selected physical transfer pattern 128 to a single locale mayinclude generating preparation instructions as a function of theselected physical transfer pattern and may include transmitting thepreparation instructions to at least an alimentary element originator.Preparation instructions may include instructions directed to alimentaryelement originators based on when to prepare and/or generate eachalimentary element and to which physical transfer apparatus 112 eachalimentary element belongs. Computing device 104 may determine whichinstructions contain information that corresponds to responsibilitiesdelegated to alimentary element originators, including when to prepareand/or generate alimentary elements, the identity of the physicaltransfer apparatus destined to receive the alimentary element and whenthe physical transfer to the physical transfer apparatus is to occur.Computing device 104 may generate a representation of the clusterinstruction set 152 as it pertains to the alimentary element originator,as described in further detail below. Computing device 104 may transmitthe preparation instructions to at least an alimentary elementorigination via a graphical user interface, as described in furtherdetail below.

Continuing in reference to FIG. 1, determining a cluster instruction set152 for a selected physical transfer pattern 128 to a single locale mayinclude generating physical transfer instructions as a function of theselection physical transfer pattern and may include transmitting thephysical transfer instructions to at least a physical transferapparatus. Physical transfer instructions may include instructionsdirected to at least a physical transfer apparatus 112 based on where toreceive each alimentary element and the geophysical path associated withthe optimal physical transfer pattern 128 and the location and time forany interchange nodes 144. Computing device 104 may determine whichinstructions contain information that corresponds to responsibilitiesdelegated physical transfer apparatuses 112, including where to receivethe alimentary elements from originators, the identity of the alimentaryelement, the physical transfer path to the alimentary element, thelocation and times associated with any interchange nodes 144, thephysical transfer path to any interchange nodes 144, the physicaltransfer path to the single locale, and/or the identity to any of theplurality of users to which the cluster combination of alimentaryelements belongs. Computing device 104 may generate a representation ofthe cluster instruction set 152 as it pertains to the physical transferapparatuses, as described in further detail below. Cluster instructionset 152 may include instructions regarding where to receive eachalimentary element and the geophysical path associated with the optimalphysical transfer pattern 128 and the location and time for anyinterchange nodes 144. Computing device 104 may transmit the physicaltransfer instructions to at least a physical transfer apparatus via agraphical user interface, as described in further detail below.

Continuing in reference to FIG. 1, computing device 104 is configured togenerate a representation of the cluster instruction set 152 via agraphical user interface to at least a physical transfer apparatus 112and the plurality of alimentary element originators. Computing device104 may generate a representation of the cluster instruction set 152 viaa graphical user interface that includes graphics, text, and/or anyother audiovisual display and/or transmittance. A “graphical userinterface,” as used in this disclosure, is any form of a user interfacethat allows a user to interface with an electronic device throughgraphical icons, audio indicators, text-based interface, typed commandlabels, text navigation, and the like, wherein the interface isconfigured to provide information to the user and accept input from theuser. Computing device 104 may generate a representation of the clusterinstruction set 152 via a graphical user interface using any mappingapplication or algorithm, for instance and without limitation, aweb-based navigation application such, a mobile navigation application,or the like. Persons skilled in the art, upon review of this disclosurein its entirety, will be aware of the various ways in which a computingdevice 104 may display to a user a physical transfer path via agraphical user interface, and be aware the various navigationapplications that may be used to communicate a physical transfer path.

Referring now to FIGS. 2A and 2B, a non-limiting exemplary embodiment200 of a user device 204 for providing a cluster instruction set 152 isillustrated. Computing device 104 may generate, for instance and withoutlimitation, at least two different representation of a clusterinstruction set 152, wherein one representation is configured for analimentary element originator (as depicted in FIG. 2A) and onerepresentation is configured for a physical transfer apparatus (asdepicted in FIG. 2B). Computing device 104 may display clusterinstruction set 152 instruction via a user device 204, wherein a userdevice is any device that may be a computing device 104, such as a“smartphone”, laptop, tablet, or any other device with capabilities asdescribed herein. In non-limiting exemplary embodiments, user device 204may display the generated representation via a graphical user interface(GUI), wherein the GUI represents, as is the case in FIG. 2A, whichalimentary element relates to which user, when to generate and/orprepare each alimentary element, the identity of the physical transferapparatus to obtain the items, the payment status of the alimentaryelement, and the rank with which to prepare items according to thetiming of the physical transfer paths associated with a plurality ofcluster combinations 108. In non-limiting exemplary embodiments, userdevice 204 may display the generated representation via a graphical userinterface (GUI), wherein the GUI represents, as is the case in FIG. 2B,the identity and location of the alimentary element originator, theorder in which the alimentary elements of the cluster 108 are to bereceived, and the physical transfer pattern 128 associated with thephysical transfer apparatus 112.

Referring now to FIG. 3, a non-limiting exemplary embodiment 300 of acluster combination database 304 is illustrated. Cluster combinationdatabase 304 may be implemented, without limitation, as a relationaldatabase, a key-value retrieval database such as a NOSQL database, orany other format or structure for use as a database that a personskilled in the art would recognize as suitable upon review of theentirety of this disclosure. Cluster combination database 304 mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableand the like. Cluster combination database 304 may include a pluralityof data entries and/or records, as described above. Data entries in acluster combination database 304 may be flagged with or linked to one ormore additional elements of information, which may be reflected in dataentry cells and/or in linked tables such as tables related by one ormore indices in a relational database. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which data entries in a database may store, retrieve, organize,and/or reflect data and/or records as used herein, as well as categoriesand/or populations of data consistently with this disclosure.

Further referring to FIG. 3, cluster combination database 304 mayinclude, without limitation, an alimentary element table 308,interchange node table 312, physical apparatus table 316, objectivefunction table 320, transfer path table 324, and/or heuristic table 328.Determinations by a machine-learning process, machine-learning model,ranking function, mapping algorithm, and/or objective function may alsobe stored and/or retrieved from the cluster combination database 304,for instance in non-limiting examples a classifier describing aplurality of candidate transfer paths 140 as it relates to a selectedinterchange node 144, wherein a classifier is an identifier that denotesa subset of data that contains a heuristic and/or relationship, as maybe useful to system 100 described herein. Determinations by amachine-learning process for selecting a region for determining aphysical transfer pattern 128 and/or a rankings of candidate physicaltransfer paths 140 based on physical transfer apparatus availability,geolocation, timing, and the like, may also be stored and/or retrievedfrom the cluster combination database 304. As a non-limiting example,cluster combination database 304 may organize data according to one ormore instruction tables. One or more cluster combination database 304tables may be linked to one another by, for instance in a non-limitingexample, common column values. For instance, a common column between twotables of cluster combination database 304 may include an identifier ofa submission, such as a form entry, textual submission, global positionsystem (GPS) coordinates, addresses, and the like, for instance asdefined herein; as a result, a query by a computing device 104 may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of data, including types of data,names and/or identifiers of individuals submitting the data, times ofsubmission, and the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which datafrom one or more tables may be linked and/or related to data in one ormore other tables.

Still referring to FIG. 3, in a non-limiting embodiment, one or moretables of a cluster combination database 304 may include, as anon-limiting example, an alimentary element table 308, which may includegeolocations, GPS coordinates, addresses, or the like, associated withthe location of a plurality of users corresponding to a cluster 108, theidentity of the alimentary elements in said cluster 108, and/or linkedto other data such as the order destination geolocation data for thecluster combination, the alimentary element identifiers 136, and/orother elements of data computing device 104 and/or system 100 may store,retrieve, and use to determine usefulness and/or relevance of data indetermining physical transfer patterns 128, assigning physical transferapparatuses 112, and the like, as described in this disclosure. One ormore tables may include interchange node table 312, which may include ahistory of numerical values, GPS coordinates, addresses, timestamps, andthe like, for instance and without limitation, that representinterchange nodes 144 determined for physical transfer apparatuses indetermining location and time for interchange nodes 144 that may haveworked in the past. One or more tables may include a physical transferapparatus table 316, which may store and/or organize the number andidentity of physical transfer apparatuses 112, their availability,geolocation, and the like. One or more tables may include an objectivefunction table 320, which may store and/or organize which may storeand/or organize rankings, scores, models, outcomes, functions, numericalvalues, vectors, matrices, and the like, that represent determinations,optimizations, iterations, variables, and the like used in optimizing anobjective function, as described herein, including data corresponding toa plurality of constraints associated with optimizing the objectivefunction for determining a physical transfer pattern 128. One of moretables may include a transfer path table 324, which may includegeolocations, GPS coordinates, addresses, or the like, associated withone or more candidate physical transfer paths 140, the success rate ofusing a physical transfer path, and other confounding variablesassociated with following a physical transfer path including trafficpatterns, roadwork, obstacles, and the like. One or more tables mayinclude, without limitation, a heuristic table 328, which may includeone or more inputs describing potential mathematical relationshipsbetween at least an element of user data and, for instance and withoutlimitation, batching instructions, and rankings thereof, and/orpredicted paths and how they may change as a function of reachingparticle areas of a map, as described in further detail below.

Referring now to FIG. 4, an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 408 given data provided as inputs 412;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 4, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 4, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data to elementsthat characterizes a sub-population, such as a subset of physicaltransfer paths and/or other analyzed items and/or phenomena for which asubset of training data may be selected.

Still referring to FIG. 4, machine-learning module 400 may be configuredto perform a lazy-learning process 420 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 404. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 404elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 4,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4, machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude a plurality of constraints 132 and a cluster 108 as describedabove as inputs, candidate physical transfer paths 140 as outputs, and aranking function representing a desired form of relationship to bedetected between inputs and outputs; ranking function may, for instance,seek to maximize the probability that a given input and/or combinationof elements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Ranking function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 404. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 428 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 4, machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4, machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 4, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training data 404 set are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data 404.

Referring now to FIG. 5, a non-limiting exemplary embodiment 500 of auser device is illustrated. An audiovisual notification 116 may betransmitted via a graphical user interface, as described herein, to eachuser of the plurality of users, for instance and without limitation,within a particular radius of the single locale about submittingsuitable alimentary elements to the cluster combination. Submitting“suitable” alimentary elements may refer to alimentary elements thatcorrespond with the users' respective alimentary element programs 120,as described above. A “particular radius,” as used in this disclosure,is a predetermined distance that system 100 may use to allow users tosubmit to a cluster 108. In non-limiting illustrative embodiments, aparticular radius may be all users provided a login token for accessinga cluster 108, wherein the cluster 108 is started by a ‘host’ and thehost may open the cluster 108 queue for alimentary element submissionbased on some criteria. In further non-limiting illustrativeembodiments, host may send a login token to access the cluster 108 forordering via the graphical user interface. A particular radius may referto all users in an office, for instance on a particular floor of anoffice building. A particular radius may be all users connected to aparticular network, such as an internet network, internet-of-things(IOT) network, or the like, such as the tenants of a shared apartment ormembers of a single-family household. A particular radius may refer toall users of an apartment building, wherein users may submit orderswithin a specified window of time for alimentary elements to be added toa cluster 108.

Still referring to FIG. 5, audiovisual notification 116 may be providedto a user within a particular radius wherein the notification alerts auser to a cluster combination queue. The audiovisual notification 116may include information about submitting to the cluster 108, forinstance with the cluster 108 will be submitted, which may be indicatedby a countdown, timer, or the like.

Referring now to FIG. 6, an exemplary embodiment of a method 600 forgenerating a cluster instruction set is illustrated. At step 605,computing device 104 is configured for generating, as a function of areceived cluster, a plurality of physical transfer paths from a distinctplurality of initiation points to a single locale, wherein the clustercomprises a cluster of a plurality of alimentary elements for physicaltransfer to the single locale. Receiving the cluster 108 may includegenerating an audiovisual notification 116 in response to receiving thecluster 108. The audiovisual notification 116 may address a user tosubmit an alimentary element to the cluster 180, wherein the alimentaryelement corresponds to an alimentary element program 120; this may beimplemented, without limitation, as described above in reference toFIGS. 1-5.

Still referring to FIG. 6, at step 610, computing device 104 isconfigured for determining, as a function of the plurality of physicaltransfer paths, a physical transfer pattern 128, wherein determining thephysical transfer pattern 128 includes generating an objective functionof the plurality of physical transfer paths 140 as a function of aplurality of constraints 132, wherein minimizing the objective functionminimizes the plurality of physical transfer resources, selecting aphysical transfer path that minimizes objective function, anddetermining a cluster combination instruction set 152 for the physicaltransfer pattern 128 to the single destination. Determining the physicaltransfer pattern 128 may include generating a plurality of physicaltransfer paths 140 using a cluster machine-learning process 124, whereinthe cluster machine-learning process 124 generates at least a physicaltransfer path for each alimentary element of the cluster 108. Theplurality of constraints 132 may include geophysical data regarding eachapparatus of the plurality of physical transfer apparatuses 112. Usingthe cluster machine-learning process 124 to determine the physicaltransfer pattern 128 may include generating an identifier 136 for eachalimentary element of the cluster 108 of the plurality of alimentaryelements, and determining, using the identifier 136 and the objectivefunction, when each alimentary element originator should generate eachalimentary element as a function of the plurality of constraints 132 andthe plurality of physical transfer paths 140. Minimizing the pluralityof physical transfer resources may include minimizing the number ofphysical transfer apparatuses utilized while minimizing the amount oftime to perform the physical transfer pattern. Determining a physicaltransfer pattern may include using the cluster machine-learning processto identify at least an interchange node 144, wherein each interchangenode 144 of the at least an interchange node 144 comprises a nodelocation and a node time based on the physical locations of a pluralityof physical transfer apparatuses 112 prior to physical transfer to auser location, calculating a change in candidate physical transfer pathtime and distance resulting from using the interchange node 144, anddetermining which physical transfer path and interchange node 144pairing of the plurality of candidate transfer paths 140 minimizes theobjective function; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-5.

Continuing in reference to FIG. 6, determining a physical transferpattern may include using a cluster machine-learning process 124 togenerate a plurality of candidate transfer paths 140, wherein eachcandidate physical transfer path 140 is a geophysical path that aphysical transfer apparatus 112 may follow to obtain an alimentaryelement and transfer to a single locale, generating at least aninterchange node 144, wherein each interchange node 144 comprises a nodelocation and a node time based on the physical locations of a pluralityof physical transfer apparatuses 112 prior to physical transfer to auser location, calculating a change in candidate physical transfer path140 time and distance resulting from using the interchange node 144, anddetermining which physical transfer path and interchange node 144pairing of the plurality of candidate transfer paths represents anoptimal physical transfer. Determining which transfer path andinterchange node 144 pairing represents an optimal physical transferpath may include ranking, using a ranking machine-learning process, aplurality of transfer paths and a plurality of interchange nodes 144,wherein ranking is based on physical transfer resources and physicaltransfer time, and selecting the optimal physical transfer path whichminimizes physical transfer resources and physical transfer time as afunction of the ranking; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-5.

Continuing in reference to FIG. 6, at step 615, computing device 104 isconfigured for generating a representation of the cluster instructionset 152 via a graphical user interface to at least a physical transferapparatus 112 and the plurality of alimentary element originators.Determining a cluster instruction set 152 for a selected physicaltransfer pattern 128 to a single destination may include generatingpreparation instructions as a function of the selected physical transferpattern 128 and transmitting the preparation instructions to at least analimentary element originator. Determining a cluster instruction set 152for a selected physical transfer pattern 128 to a single destination mayinclude generating physical transfer instructions as a function of theselected physical transfer pattern 128 and transmitting the physicaltransfer instructions to at least a physical transfer apparatus 112;this may be implemented, without limitation, as described above inreference to FIGS. 1-5.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for generating a cluster combinationinstruction set using machine learning, the system comprising acomputing device, wherein the computing device is configured to: producean alimentary element program, wherein producing the alimentary elementprogram further comprises: receiving at least one biological extractiondatum from a user; generating the alimentary element program as afunction of the at least one biological extraction datum; and displayingthe alimentary element program; generate, as a function of a receivedcluster, a plurality of physical transfer paths from a series ofdistinct initiation points to a single locale, wherein the clustercomprises a cluster of a plurality of alimentary elements originatingfrom a plurality of establishments for physical transfer to the singlelocale at the same time; determine, as a function of the plurality ofphysical transfer paths, a physical transfer pattern, whereindetermining the physical transfer pattern comprises: generating anobjective function of the plurality of physical transfer paths as afunction of a plurality of constraints, wherein minimizing the objectivefunction minimizes a plurality of physical transfer resources; selectinga physical transfer path that minimizes the objective function by usinga machine-learning process, wherein the machine-learning process isconfigured to receive a plurality of transfer paths as inputs and assigneach of the plurality of transfer paths a score as a function of apredetermined variable and outputs a ranked lists of outputs as afunction of a ranking criteria, wherein the ranking criteria includesthe least amount of physical transfer apparatuses required; anddetermining a cluster combination instruction set for the physicaltransfer pattern to the single locale; and generate a representation ofthe cluster combination instruction set via a graphical user interfaceto at least a physical transfer apparatus and the plurality ofestablishments.
 2. The system of claim 1, wherein receiving the clusterfurther comprises generating an audiovisual notification in response toreceiving the cluster.
 3. The system of claim 2, wherein the audiovisualnotification addresses the user to submit an alimentary element to thecluster, wherein the alimentary element corresponds to an alimentaryelement program.
 4. The system of claim 1, wherein determining thephysical transfer pattern further comprises generating a plurality ofphysical transfer paths using a cluster machine-learning process,wherein the cluster machine-learning process generates at least aphysical transfer path for each alimentary element of the cluster. 5.The system of claim 1, wherein the plurality of constraints furthercomprises geophysical data regarding each physical transfer apparatus.6. The system of claim 1, wherein using the cluster machine-learningprocess to determine the physical transfer pattern further comprises:generating an identifier for each alimentary element of the cluster ofthe plurality of alimentary elements; and determining, using theidentifier and the objective function, when each alimentary elementoriginator should generate each alimentary element as a function of theplurality of constraints and the plurality of physical transfer paths.7. The system of claim 1, wherein minimizing the plurality of physicaltransfer resources further comprises minimizing the number of physicaltransfer apparatuses utilized while minimizing the amount of time toperform the physical transfer pattern.
 8. The system of claim 1, whereindetermining a physical transfer pattern further comprises: using thecluster machine-learning process to identify at least an interchangenode, wherein each interchange node of the at least an interchange nodecomprises a node location and a node time based on the physicallocations of a plurality of physical transfer apparatuses prior tophysical transfer to the user location; calculating a change incandidate physical transfer path time and distance resulting from usingthe interchange node; and determining which physical transfer path andinterchange node pairing of the plurality of candidate transfer pathsminimizes the objective function.
 9. The system of claim 1, whereindetermining a cluster instruction set for the selected physical transferpattern to a single locale further comprises: generating preparationinstructions as a function of the selected physical transfer pattern;and transmitting the preparation instructions to at least an alimentaryelement originator.
 10. The system of claim 1, wherein determining acluster instruction set for the selected physical transfer pattern to asingle locale further comprises: generating physical transferinstructions as a function of the selected physical transfer pattern;and transmitting the physical transfer instructions to at least aphysical transfer apparatus.
 11. A method for generating a clustercombination instruction set using machine learning, the methodcomprising a computing device, wherein the computing device isconfigured for: producing an alimentary element program, whereinproducing the alimentary element program further comprises: receiving atleast one biological extraction datum from a user; generating thealimentary element program as a function of the at least one biologicalextraction datum; and displaying the alimentary element program;generating, as a function of a received cluster, a plurality of physicaltransfer paths from a series of distinct initiation points to a singlelocale, wherein the cluster comprises a cluster of a plurality ofalimentary elements originating from a plurality of establishments forphysical transfer to the single locale at the same time; determining, asa function of the plurality of physical transfer paths, a physicaltransfer pattern, wherein determining the physical transfer patterncomprises: generating an objective function of the plurality of physicaltransfer paths as a function of a plurality of constraints, whereinminimizing the objective function minimizes a plurality of physicaltransfer resources; selecting a physical transfer path that minimizesobjective by using a machine-learning process, wherein themachine-learning process is configured to receive a plurality oftransfer paths as inputs and outputs a ranked lists of outputs as afunction of a ranking criteria, wherein the ranking criteria includesthe least amount of physical transfer apparatuses required; anddetermining a cluster combination instruction set for the physicaltransfer pattern to the single destination; and generating arepresentation of the cluster combination instruction set via agraphical user interface to at least a physical transfer apparatus andthe plurality of establishments.
 12. The method of claim 11, whereinreceiving the cluster further comprises generating an audiovisualnotification in response to receiving the cluster.
 13. The method ofclaim 12, wherein the audiovisual notification addresses a user tosubmit an alimentary element to the cluster, wherein the alimentaryelement corresponds to an alimentary element program.
 14. The method ofclaim 11, wherein determining the physical transfer pattern furthercomprises generating a plurality of physical transfer paths using acluster machine-learning process, wherein the cluster machine-learningprocess generates at least a physical transfer path for each alimentaryelement of the cluster.
 15. The method of claim 11, wherein theplurality of constraints further comprises geophysical data regardingeach apparatus of the plurality of physical transfer apparatuses. 16.The method of claim 11, wherein using the cluster machine-learningprocess to determine the physical transfer pattern further comprises:generating an identifier for each alimentary element of the cluster ofthe plurality of alimentary elements; and determining, using theidentifier and the objective function, when each alimentary elementoriginator should generate each alimentary element as a function of theplurality of constraints and the plurality of physical transfer paths.17. The method of claim 11, wherein minimizing the plurality of physicaltransfer resources further comprises minimizing the number of physicaltransfer apparatuses utilized while minimizing the amount of time toperform the physical transfer pattern.
 18. The method of claim 11,wherein determining a physical transfer pattern further comprises: usingthe cluster machine-learning process to identify at least an interchangenode, wherein each interchange node of the at least an interchange nodecomprises a node location and a node time based on the physicallocations of a plurality of physical transfer apparatuses prior tophysical transfer to a user location; calculating a change in candidatephysical transfer path time and distance resulting from using theinterchange node; and determining which physical transfer path andinterchange node pairing of the plurality of candidate transfer pathsminimizes the objective function.
 19. The method of claim 11, whereindetermining a cluster instruction set for the selected physical transferpattern to a single locale further comprises: generating preparationinstructions as a function of the selected physical transfer pattern;and transmitting the preparation instructions to at least an alimentaryelement originator.
 20. The method of claim 11, wherein determining acluster instruction set for the selected physical transfer pattern to asingle locale further comprises: generating physical transferinstructions as a function of the selected physical transfer pattern;and transmitting the physical transfer instructions to at least aphysical transfer apparatus.